Leaders and business intelligence at credit unions are putting a tremendous focus on ways to use advanced data analytics to identify trends, detect patterns and glean other valuable findings from the sea of information available to them. Without question, member data is valuable. But the greatest value lies in the ability to empower each line of business to achieve strategic initiatives and performance goals. When this empowerment is coupled with improving member service, a proven, repeatable best practice results.
Today's “analytics” are often visuals, graphs and trends while advanced analytics, while a journey, result in quantitatively-proven prescriptive statistics from regression and machine learning. Advanced analytics enable the credit union to effectively segment members to identify opportunities, improve service and retention, and target new products and services.
Start the advanced analytics journey with the end goal in mind and consider these seven, “top-down” best practices as a roadmap to guide the credit union through each step.
1. Empower. Advanced analytics put actionable information in the hands of lenders and frontline employees. This drives immediate, responsive actions that build member loyalty and help team members succeed. When the lines of business benefit from measured prescriptive action lists – management will view these lists as a critical tool to align the credit union's strategic goals, create score cards, identify top performers and move the corporate culture toward making more data driven decisions. To provide prescriptive action lists, the credit union should consider the channel, message, timing and approach. The key to success with advanced analytics is the ability to provide the right value for the right member, at the right time, through the right channel, using the right message. Prescriptive statistics is beyond a simple probability based on past results. A prescriptive action list is coveted by loan officers and branches because they have confidence that taking action results in the best opportunity for success.
2. Refine: Once a set of successful strategies and best practices for actions are set, it's time to further refine those actions that lead to successful outcomes. This results in a predictable and positive outcome that improves member service and/or achieves growth, revenue, retention or risk management goals.
3. Apply: Is there a strategy to apply advanced analytics? The opportunities to generate business value from data and analytics are significant. Having a chief strategy officer and chief analytics officer on your team will allow you to curate your most critical information assets; an outsourced CSO/CAO may be a good option for many credit unions. Start by building statistical methods to measure the right action on the right segment, and using the right channel, message and timing. Measuring these results within each segment is key. Segments should be based on contribution and other algorithms coupled with usage determinations that assign the member to millennial, executive and more. A feedback loop results by using these statistics as the credit union further segments and refines the message, channel, timing and approach to create a set of recurring best practices. Random Forest, logistic and linear regression may reveal a portion of the segment that responds well to the action to then isolate that portion of the segment and repeat, which results in future prescriptive action lists.
4. Take action. Once key data segments have been identified, can the credit union take action using digital channels in addition to traditional branch, statement and printed direct mail marketing? Digital channels include: SMS/text, email, mobile and internet banking calls for action. The credit union should be able to monitor if the member clicks and answers the “call to action” on a specific channel and sets as their preference. It's important to track, monitor and measure the success of incremental actions and campaigns that result in recurring best practices. Lines of business will be able to uncover the best actions, repeat and refine them over time for continuous improvement and results.
5. Compute: Is there a process in place to compute the data? How the credit union computes the necessary results to properly segment data will lead to improved decision-making capabilities and positive business outcomes. Examples of these results include member contribution and how much the member is engaged. An extreme minority of members provide an extreme majority of contribution – typically the one to five percentiles provide 100% to 220% of total contribution. Percentiles five to 89 often net to zero while the bottom 10% generate a negative contribution that leaves the 100%. Different actions are prudent within these different segments. The credit union needs to have an effective way to compute advanced analytic algorithms from the data to identify trends and extract actionable information, enabling the credit union's drive to transformation.
6. Consume: How will the data be consumed? After the data is collected and cleansed, the credit union uses a BI platform, or reports, to present the information for consumption by the end user. The information needs to deliver timely information. Consumption of the data is critical in transforming the member's data into an asset so lines of business can make data-driven decisions, leading to improved member service and growth.
7. Lead: Having the right talent, team and technology makes all the difference. Many credit unions that plan to, or already have implemented an analytics department have done so by leveraging existing management and personnel. They soon realize the hard way, they need to bring in fresh, specialized leadership. Start this process by identifying skill gaps, formalizing leadership roles and implementing a hiring, training or vetting process for outsourced expertise. Many credit unions that possess even the most advanced analytics capabilities cannot hire enough people to generate the right insights from member data. Be sure the credit union has a knowledgeable data analytics team that can properly collect, prepare, manage and analyze the data from multiple silos.
The biggest financial institutions have poured millions of dollars into their advanced analytics investment by hiring teams of analysts and data scientists. They are building massive platforms to extract value from member/customer data. Since your best members could be the competition's best targets, it's important to use these best practices in your advanced analytics roadmap. Make sure you are ready to compete by making more data driven decisions through analytics to find new ways to truly understand members’ needs and optimize business results.
This article was originally published on CUtimes.com.